基于ELM-CBR的采煤工作面顶板冒落危险性预测与管理  

Hazard Prediction and Management of Roof Caving in Coal Mining Faces Based on ELM-CBR

作　　者：刘璐 季嘉琪 苗德俊[1] Liu Lu;Ji Jiaqi;Miao Dejun(School of Safety and Environmental Engineering,Shandong University of Science and Technology,Shandong Qingdao 266590)

机构地区：[1]山东科技大学安全与环境工程学院,山东青岛266590

出　　处：《山东煤炭科技》2023年第11期166-170,共5页Shandong Coal Science and Technology

摘　　要：为准确预测顶板冒落危险性等级,有针对性地进行风险管理,通过极限学习机(ELM)和案例推理(CBR)两种方法,提出相应的预测与管理方法,并将该方法得到的结果与实际情况进行对比分析。结果表明,该方法达到了较高的准确率,对预测结果进一步管理,为管理者提供决策依据。To accurately predict the hazard level of roof caving and carry out targeted risk management,corresponding prediction and management methods are proposed through two methods:extreme learning machine(ELM)and case based reasoning(CBR),and the results obtained by this method are compared and analyzed with the actual situation.The results show that this method achieves high accuracy,further manages the prediction results,and provides decision-making basis for managers.

关 键 词：极限学习机 顶板冒落 案例推理 危险性预测 

分 类 号：TD771[矿业工程—矿井通风与安全]